Quasi Maximum-Likelihood Estimation Of Dynamic Panel Data Models For Short Time Series
This paper establishes the almost sure convergence and asymptotic normality of quasi maximum-likelihood (QML) estimators of a dynamic panel data model when the time series for each cross section is short. The QML estimators are robust with respect to initial conditions and misspecification of the log-likelihood, and results are provided for a general specification of the error variance-covariance matrix. The paper also provides procedures for computing QML estimates that improve on computational methods previously recommended in the literature. Moreover, it compares the finite sample performance of several QML estimators, the differenced GMM estimator, and the system GMM estimator.
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